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  <title>Trains on an entire dataset, for a period of time using the Cascade2 training algorithm</title>

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</div><hr /><div id="function.fann-cascadetrain-on-data" class="refentry">
 <div class="refnamediv">
  <h1 class="refname">fann_cascadetrain_on_data</h1>
  <p class="verinfo">(PECL fann &gt;= 1.0.0)</p><p class="refpurpose"><span class="refname">fann_cascadetrain_on_data</span> &mdash; <span class="dc-title">Trains on an entire dataset, for a period of time using the Cascade2 training algorithm</span></p>

 </div>

 <div class="refsect1 description" id="refsect1-function.fann-cascadetrain-on-data-description">
  <h3 class="title">说明</h3>
  <div class="methodsynopsis dc-description">
   <span class="type">bool</span> <span class="methodname"><strong>fann_cascadetrain_on_data</strong></span>
    ( <span class="methodparam"><span class="type">resource</span> <code class="parameter">$ann</code></span>
   , <span class="methodparam"><span class="type">resource</span> <code class="parameter">$data</code></span>
   , <span class="methodparam"><span class="type">int</span> <code class="parameter">$max_neurons</code></span>
   , <span class="methodparam"><span class="type">int</span> <code class="parameter">$neurons_between_reports</code></span>
   , <span class="methodparam"><span class="type">float</span> <code class="parameter">$desired_error</code></span>
   )</div>

  <p class="para rdfs-comment">
   The cascade output change fraction is a number between 0 and 1 determining how large a fraction
   the <span class="function"><a href="function.fann-get-mse.html" class="function">fann_get_MSE()</a></span> value should change within <span class="function"><a href="function.fann-get-cascade-output-stagnation-epochs.html" class="function">fann_get_cascade_output_stagnation_epochs()</a></span>
   during training of the output connections, in order for the training not to stagnate.  If the training stagnates,
   the training of the output connections will be ended and new candidates will be prepared.
  </p>
  <p class="para">
   This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm
   as it’s internal training algorithm. This algorithm can be set to either <strong><code>FANN_TRAIN_RPROP</code></strong> or
   <strong><code>FANN_TRAIN_QUICKPROP</code></strong> by <span class="function"><a href="function.fann-set-training-algorithm.html" class="function">fann_set_training_algorithm()</a></span>, and the parameters
   set for these training algorithms will also affect the cascade training.
  </p>
 </div>


 <div class="refsect1 parameters" id="refsect1-function.fann-cascadetrain-on-data-parameters">
  <h3 class="title">参数</h3>
  <dl>

   
    <dt>
<em><code class="parameter">ann</code></em></dt>

    <dd>

     <p class="para">Neural network <span class="type"><a href="language.types.resource.html" class="type resource">resource</a></span>.</p>
    </dd>

   
   
    <dt>
<em><code class="parameter">data</code></em></dt>

    <dd>

     <p class="para">Neural network training data <span class="type"><a href="language.types.resource.html" class="type resource">resource</a></span>.</p>
    </dd>

   
   
    <dt>
<em><code class="parameter">max_neurons</code></em></dt>

    <dd>

     <p class="para">
      The maximum number of neurons to be added to neural network.
     </p>
    </dd>

   
   
    <dt>
<em><code class="parameter">neurons_between_reports</code></em></dt>

    <dd>

     <p class="para">
      The number of neurons between printing a status report. A value of zero means no reports should be printed.
     </p>
    </dd>

   
   
    <dt>
<em><code class="parameter">desired_error</code></em></dt>

    <dd>

     <p class="para">
      The desired <span class="function"><a href="function.fann-get-mse.html" class="function">fann_get_MSE()</a></span> or <span class="function"><a href="function.fann-get-bit-fail.html" class="function">fann_get_bit_fail()</a></span>,
      depending on which stop function is chosen by <span class="function"><a href="function.fann-set-train-stop-function.html" class="function">fann_set_train_stop_function()</a></span>
     </p>
    </dd>

   
  </dl>

 </div>


 <div class="refsect1 returnvalues" id="refsect1-function.fann-cascadetrain-on-data-returnvalues">
  <h3 class="title">返回值</h3>
  <p class="para">Returns <strong><code>TRUE</code></strong> on success, or <strong><code>FALSE</code></strong> otherwise.</p>
 </div>


 <div class="refsect1 seealso" id="refsect1-function.fann-cascadetrain-on-data-seealso">
  <h3 class="title">参见</h3>
  <p class="para">
   <ul class="simplelist">
    <li class="member"><span class="function"><a href="function.fann-train-on-data.html" class="function" rel="rdfs-seeAlso">fann_train_on_data()</a> - Trains on an entire dataset for a period of time</span></li>
    <li class="member"><span class="function"><a href="function.fann-cascadetrain-on-file.html" class="function" rel="rdfs-seeAlso">fann_cascadetrain_on_file()</a> - Trains on an entire dataset read from file, for a period of time using the Cascade2 training algorithm.</span></li>
   </ul>
  </p>
 </div>



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